Fetch prices

files <- list.files(path = "tmp", pattern = "*.json", full.names = TRUE)
stopifnot(length(files) > 0)
files

##  [1] "tmp/BCH.json"    "tmp/BCHABC.json" "tmp/BTC.json"   
##  [4] "tmp/EOS.json"    "tmp/ETC.json"    "tmp/ETH.json"   
##  [7] "tmp/LTC.json"    "tmp/REP.json"    "tmp/XLM.json"   
## [10] "tmp/XRP.json"    "tmp/ZRX.json"

extract <- function(f){
  json = fromJSON(file = f)
  sapply(json$Data, function(x) x$open)
}

prices <-as.data.frame(sapply(files, extract))

Linear regression

linear_regression <- function(y) {
  x <- 1:length(y) / 60
  relation <- lm(y ~ x)
  plot(x, y, col = "darkgreen", abline(relation), xlab = "hours", ylab = "USD")
}

# names(prices[,1])
sapply(prices, linear_regression)

## $`tmp/BCH.json`
## NULL
## 
## $`tmp/BCHABC.json`
## NULL
## 
## $`tmp/BTC.json`
## NULL
## 
## $`tmp/EOS.json`
## NULL
## 
## $`tmp/ETC.json`
## NULL
## 
## $`tmp/ETH.json`
## NULL
## 
## $`tmp/LTC.json`
## NULL
## 
## $`tmp/REP.json`
## NULL
## 
## $`tmp/XLM.json`
## NULL
## 
## $`tmp/XRP.json`
## NULL
## 
## $`tmp/ZRX.json`
## NULL

Normalise

normalise <- function(y) {
  low <- min(y)
  high <- max(y)
  y <- y - low
  y <- y / (high - low)
}

normalised <- sapply(prices, normalise)

matplot(normalised, pch = 1)